Allogeneic hematopoietic cell transplantation for myelofibrosis in the era of JAK inhibitors
Bibliographic record
Abstract
The discovery of JAK2617F mutation paved the way for the development of small molecule inhibitors of JAK1/2 resulting in first approved JAK1/2 inhibitor, ruxolitinib, for the treatment of patients with myelofibrosis (MF). Although JAK1/2 inhibitor therapy is effective in decreasing the burden of symptoms associated with splenomegaly and MF-related constitutional symptoms, it is neither curative nor effective in reducing the risk of leukemic transformation. Presently, allogeneic hematopoietic cell transplantation (HCT) is the only curative therapy for MF. A significant risk of regimen-related toxicities, graft failure, and GVHD are major barriers to the success of HCT in MF. Because of significant HCT-associated morbidity and mortality, divergent opinions regarding its appropriate role in this clinical situation have emerged. In this review, the risk-benefit ratios of modern drug therapy compared with HCT in MF patients are analyzed. A risk-adapted approach individualized to each patient's biologic characteristics and comorbidities is described, which is currently warranted in determining optimal treatment strategies for patients with MF. Inclusion of JAK1/2 inhibitor therapy in future transplant conditioning regimens may provide an opportunity to overcome some of these barriers, resulting in greater success with HCT for MF patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".